Construction and application of artificial airway stability scheme forintra-hospital transport of neurocritically ill patients

Corresponding author: zhujinjun, 31441162@qq.com

DOI: 10.12201/bmr.202410.00001

•  

  Abstract: Objective To construct an artificial airway stability scheme for in-hospital transport of patients with severe neurological diseases, and to explore the application effect. Methods Using non-concurrent control study design, select June 2022-September 2023 admitted to our hospital neurosurgery intensive care unit (NICU), complete hospital transfer to CT, MRI examination area of artificial airway nerve severe patients as the research object, will be in June 2022-January 2023 104 patients as the control group, receiving regular transport care; from February to September 2023, 106 patients were in the intervention group, and the nosocomial transport artificial airway stability scheme was implemented for NICU patients. The incidence of airway-related adverse events, transit time and family satisfaction were compared between the two groups. Results The incidence of airway-related adverse events in the intervention group (1.9%) was lower than that in the control group (10.4%); The transit time in the intervention group (19.08±3.17) was faster than that in the control group (25.50±4.84); the family satisfaction of the intervention group (99.0%) was higher than

  Key words: ; ; ; 

  Submit time: 1 October 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
• html

• 图表

• ZHU yan, ZHENG Jie, Li Xiaoying, Yang Xiaolin, HE Yongqun. Introduction to Basic Formal Ontology and its Chinese Translation. 2020. doi: 10.12201/bmr.202007.00009

  zhangchen, chenlihai, caoyuanyuan. The application of 3D printing difficult airway model based on Kirkpatrick Model in anesthesiology intern training course. 2024. doi: 10.12201/bmr.202409.00045

  dongtingting. Study on the safety impact and countermeasures of medical artificial intelligence products on patients. 2020. doi: 10.12201/bmr.202012.00004

  zhaorui, shixiuyuan, zhongxueran, renping, tianxueqin, liuchunping, youmao. Construction of clinical application evaluation path of medical artificial intelligence technology based on health technology evaluationRui Zhao, Xiuyuan Shi, Xueran Zhong, Chunping Liu, Ping Ren, Xueqin Tian, Mao You. 2022. doi: 10.12201/bmr.202203.00005

  GE Xiaoling. Application of Artificial Intelligence Large Models in Healthcare:a Survey. 2024. doi: 10.12201/bmr.202408.00039

  ZhengYanli, Han Fuhai, LI Shuyu, SU Wenxing. Application Status and Prospect of Artificial Intelligence Large Models in Medicine. 2023. doi: 10.12201/bmr.202312.00027

  Dong Kun, Yang Fen, Yang Yang. Application and Thinking of Artificial Intelligence in General Practitioner Training. 2023. doi: 10.12201/bmr.202305.00011

  Li Ping, Sun Liping, Ren He, Li Huaping, Shao Zeguo. Construction of artificial intelligence thinking training course system for medical device specialty. 2020. doi: 10.12201/bmr.202007.00013

  shisenzhong. Analysis of the Risks and Governance Strategies for the Application of Generative Artificial Intelligence (GAI) in Primary Healthcare. 2024. doi: 10.12201/bmr.202408.00053

  xiejunxiang. The composition of Key Technologies and Application in Medical Artificial Intelligence. 2020. doi: 10.12201/bmr.202005.00243

• ID	Submit time	Number	Download
  1	2024-09-10	bmr.202410.00001V1	Download

• Public  Anonymous  To author only Submit Comments